Since the introduction of electrical power distribution systems in the late 19th century, there has been a need to monitor their operational and electrical characteristics. The ability to collect, analyze, and respond to information about the electrical power system can improve safety, minimize equipment loss, decrease scrap, and ultimately save time and money. To that end, monitoring devices were developed to measure and report such information. With the dawn of the electronics age, the quality and quantity of data from monitoring devices was vastly improved, and communications networks and software were developed to collect, display and store information. Unfortunately, those responsible for evaluating data from monitoring devices are now overwhelmed by information from their monitoring systems. In the endeavor to maximize the usefulness of a monitoring system, monitoring equipment manufacturers are seeking methods of presenting information in the most useful format.
Effectively monitoring today's electrical power distribution systems is cumbersome, expensive, and inefficient. Electric power monitoring systems are typically arranged in a hierarchy with monitoring devices such as electrical meters installed at various levels of the hierarchy. Monitoring devices measure various characteristics of the electrical signal (e.g., voltage, current, waveform distortion, power, etc.) passing through the conductors, and the data from each monitoring device is analyzed by the user to evaluate potential performance or quality-related issues. However, the components of today's electrical monitoring systems (monitoring devices, software, etc.) act independently of each other, requiring the user to be an expert at configuring hardware, collecting and analyzing data, and determining what data is vital or useful. There are two problems here: the amount of data to be analyzed and the context of the data. These are separate but related issues. It is possible to automate the analysis of the data to address the amount of data. But, in order to do this reliably, the data must be put into context. The independence of data between each monitoring device evaluating the electrical system essentially renders each monitoring device oblivious of data from other monitoring devices connected to the system being analyzed. Accordingly, the data transmitted to the system computer from each monitoring device is often misaligned in that data from each monitoring device on the system does not arrive at the monitoring system's computer simultaneously. There are two basic reasons for the temporal misalignment of data between monitoring devices: communications time delays and monitoring device timekeeping & event time stamping. It is then up to the user to analyze and interpret this independent data in order to optimize performance or evaluate is potential quality-related concerns on the electrical system.
Sophisticated processing capabilities in digital monitoring devices allow large amounts of complex electrical data to be derived and accumulated from a seemingly simple electrical signal. Because of the data's complexity, quantity, and relative disjointed relationship from one monitoring device to the next, manual analysis of all the data is an enormous effort that often requires experts to be hired to complete the task. This process is tedious, complex, prone to error and oversight, and time-consuming. A partial solution has been to use global positioning satellite (GPS) systems to timestamp an event, but this approach requires that the user purchase and install additional hardware and data lines to link the monitoring devices together. And this solution still requires the evaluation of large amounts of data because the system is only temporally in context; not spatially in context. Synchronizing data using GPS systems is also disadvantageous because of time delays associated with other hardware in the system. Furthermore, any alignment of data by a GPS-based system can only be as accurate as the propagation delay of the GPS signal, which means that the data still may not be optimally aligned when a GPS system is used.
The addition of supplemental monitoring devices in the electrical system does nothing more than generate more information about the electrical system at the point where the meter is added in the electrical system, increasing complexity without any benefit. Any usefulness of the data is generally limited to the locality of the monitoring device that was added, while even more data is amassed.
The complexity of many electrical systems usually necessitates an involved configuration process of monitoring systems because each metered point in the electrical system has different characteristics, which is why multiple monitoring devices are installed in the first place. As a result of the enormous volume of complex data accumulated from electrical monitoring systems heretofore, a thorough analysis of the data is typically not feasible due to limited resources, time, and/or experience.
Temporal alignment of the data is one important aspect to understand and characterize the power system. Another important aspect is having a thorough knowledge of the power monitoring system's layout (or hierarchy). Power monitoring devices measure the electrical system's operating parameters, but do not provide information about how the parameters at different points on the power monitoring system relate to each other. Knowing the hierarchy of the power monitoring system puts the operating parameters of multiple monitoring devices into context with each other.
To determine the layout of a power monitoring system, a user must review electrical one-line drawings or physically perform an inventory of the electrical system if one-line drawings are unavailable. The user manually enters the spatial information into the monitoring system software for analysis. When a new device or monitored load is added or moved within the power monitoring system, the user must manually update the monitoring system software to reflect the new addition or change.
Data alignment and layout information are essential to understanding and characterizing the power system. With these two pieces of information, the data from each meter can be integrated and put into context with every other meter in the power system. Heretofore, the only techniques for passably integrating data were complex, expensive, manually intensive, and time-consuming for the user. These techniques also permit only limited integration of data and require additional hardware (such as GPS hardware), data lines, and supplemental monitoring device accessories.
A particular issue is the occurrence of harmonic distortion on an electrical system. Harmonic distortion results in many potential electrical vulnerabilities including equipment misoperation, degradation, and potentially, failure. As more and more non-linear loads are connected to the electrical grid, issues associated with harmonic distortion will substantially increase—even in facilities that were previously not susceptible to harmonic distortion. There are various sources of harmonic distortion, most which are non-linear loads. A couple sources of harmonic distortion in an electrical power system include switch mode power supplies and silicon-controlled rectifier (SCR) controlled loads. High-impedance sources and inadequate electrical wiring can exacerbate harmonic distortion concerns. Because most loads are designed to operate most effectively at or near some designed nominal frequency, such loads may not operate as effectively when other frequencies are induced into the system. A few problems that can occur as a result of harmonic distortion include lack of phase synchronization, undervoltage circuit activation, interferences, control problems, etc.
One existing method for analyzing harmonic distortion requires measurements of the harmonic component frequencies at various points on the electrical grid. The measured data from various meters may or may not be synchronized with each other within a temporal or pseudo-temporal context. Temporal alignment is more precise than pseudo-temporal alignment. Pseudo-temporal alignment takes data within an acceptable range based on load changes in the system. However, because harmonic distortion is a steady-state phenomenon, precise temporal alignment may not be necessary. Furthermore, the measured harmonic distortion data from various meters may or may not be analyzed within a spatial context. In any case, harmonic distortion data is not currently analyzed to take advantage of both spatial and temporal or pseudo-temporal alignment of monitoring data from multiple devices, and thus limits the useful information and analysis that may be achieved. A second presently known method for performing harmonic distortion analysis of an electrical system requires purchasing a special software package that allows the user (or his/her consultant) to develop a model of the electrical system. Modeling of the electrical system requires a matrix of nodes with the corresponding impedances between the nodes. Altering the system (e.g., adding another circuit, altering the number of conductors, adding or removing components) requires that the matrix be updated to reflect the new modification. In addition, manual changes must be made to the harmonic distortion analysis software package. Such software is difficult to use and alterations are expensive and resource consuming.
What is needed, therefore, is an automated harmonic distortion analysis system for detecting and evaluating harmonic distortion in an electrical system. There is a further need for a harmonic distortion evaluation system which is easily adaptable to modifications of the electrical system. There is also a need for an integrated harmonic distortion analysis and evaluation system.